检索结果-内蒙古大学图书馆

Detection of malaria parasite in thick blood smears using deep learning

Materials Today: Proceedings 2022年 64卷 511-516页

作者： Balaram, Allam Silparaj, Manda Gajula, Rajender Department of Information Technology MLR Institute of Technology Hyderabad 500043 India Department of Computer Science and Engineering Vignan Institute of Technology and Science Hyderabad 508284 India Department of Computer Science and Engineering Vignana Bharathi Engineering College Hyderabad 501510 India

We're working on a deep learning algorithm which can distinguish between malaria parasites in blood smears in a video format which consists of several images. There are two processing steps in our method. Initially, we locate parasites by quickly inspecting a thick smear image and remove noise from that parasite. Then, using a customized Convolutional Neural Network (CNN), each image in a video is classified as one of two ways, whether it consists of parasite or not. We took a datafile of 1817 smear photographs from 148 victims from an openly accessible institute. This datafile was used to assess and trial our algorithm. © 2022

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Changes in physiology, antioxidant system, and gene expression in Microcystis aeruginosa under fenoxaprop-p-ethyl stress

引用

Environmental science and Pollution Research 2024年第19期31卷 28754-28763页

作者： Liu, Sijia Ni, Jiawei Guan, Ying Tao, Jianwei Wu, Liang Hou, Meifang Wu, Shichao Xu, Wenwu Zhang, Chu Ye, Jing School of Chemical and Environmental Engineering Shanghai Institute of Technology Shanghai201418 China Los Angeles Regional Water Quality Control Board Los AngelesCA90013 United States School of Ecological Technology and Engineering Shanghai Institute of Technology Shanghai201418 China School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai201418 China

Fenoxaprop-p-ethyl (FE) is one of the typical aryloxyphenoxypropionate herbicides. FE has been widely applied in agriculture in recent years. Human health and aquatic ecosystems are threatened by the cyanobacteria blooms caused by Microcystis aeruginosa, which is one of the most common cyanobacteria responsible for freshwater blooming. Few studies have been reported on the physiological effects of FE on M. aeruginosa. This study analyzed the growth curves, the contents of chlorophyll a and protein, the oxidative stress, and the microcystin-LR (MC-LR) levels of M. aeruginosa exposed to various FE concentrations (i.e., 0, 0.5, 1, 2, and 5 mg/L). FE was observed to stimulate the cell density, chlorophyll a content, and protein content of M. aeruginosa at 0.5- and 1-mg/L FE concentrations but inhibit them at 2 and 5 mg/L FE concentrations. The superoxide dismutase and catalase activities were enhanced and the malondialdehyde concentration was increased by FE. The intracellular (intra-) and extracellular (extra-) MC-LR contents were also affected by FE. The expression levels of photosynthesis-related genes psbD1, psaB, and rbcL varied in response to FE exposure. Moreover, the expressions of microcystin synthase-related genes mcyA and mcyD and microcystin transportation-related gene mcyH were significantly inhibited by the treatment with 2 and 5 mg/L FE concentrations. These results might be helpful in evaluating the ecotoxicity of FE and guiding the rational application of herbicides in modern agriculture. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Gene expression

来源：评论

学校读者我要写书评

暂无评论

Hybrid Golden Jackal and Whale Optimization Algorithm (HGJWOA) for Optimizing Resource and Service Allocation in Edge Computing Environments

Hybrid Golden Jackal and Whale Optimization Algorithm (HGJWO...

引用

2024 International Conference on Knowledge engineering and Communication systems, ICKECS 2024

作者： Saradha, M. Sasivanan, T. Monishkumar, C. Muralidharan, J. Nivitha, K. K. S. Rangasamy College of Technology Computer Science and Engineering Tiruchengode637 215 India Vellore Institute of Technology Computer Science and Engineering Vellore632 014 India

ISBN: (纸本)9798350359688

Edge computing has emerged as a promising paradigm to address the latency and bandwidth constraints of traditional cloud computing by bringing computation and storage closer to the data source. In edge computing environments, efficient resource and service allocation are crucial for optimizing performance and meeting user requirements. This paper proposes a novel approach for optimizing resource and service allocation in edge computing environments using a hybrid algorithm (HGJWOA)combining Golden Jackal Optimization (GJO) and Whale Optimization Algorithm (WOA). The hybridization of GJO and WOA aims to leverage the strengths of both algorithms to achieve superior optimization performance. We develop a framework for resource and service allocation, formulate the optimization problem, and define evaluation metrics. The hybrid algorithm is integrated into the framework and evaluated through extensive simulations. Experimental results demonstrate the effectiveness of the proposed approach in improving resource utilization, reducing latency, and enhancing service delivery in edge computing environments compared to traditional optimization algorithms. Additionally, case studies and real-world deployment scenarios illustrate the practical applicability and benefits of the hybrid algorithm. Overall, the proposed hybrid Golden Jackal and Whale Optimization Algorithm presents a promising solution for optimizing resource and service allocation in edge computing environments, contributing to the advancement of efficient and scalable edge computing systems. © 2024 IEEE.

关键词： Mobile edge computing

来源：评论

学校读者我要写书评

暂无评论

Leveraging U-Net Variants for High-Precision Flood Mapping: A Performance Evaluation on the ETCI Dataset 2

Leveraging U-Net Variants for High-Precision Flood Mapping: ...

引用

2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Thirugnanasammandamoorthi, Puviyarasi Ghosh, Debabrata Dewangan, Ram Kishan Thapar Institute of Engineering and Technology Department of Electronics and Communication Engineering Punjab India Bennett University School of Computer Science Engineering and Technology India

ISBN: (纸本)9798331534950

Flood mapping is a crucial task in mitigating the adverse impacts of flooding by providing accurate spatial information about inundated areas. This study explores flood mapping using the ETCI dataset, leveraging the U-Net model and its enhanced variants. Initially, a basic U-Net architecture is employed to generate flood maps, followed by progressively improved versions incorporating batch normalization, dropout, and attention mechanisms. These modifications aim to enhance model generalization, robustness, and feature extraction capabilities. For the purpose of evaluating the model's performance, metrics like accuracy, F1-score, and Intersection over Union (IoU) are utilised. The generated flood maps demonstrate the potential of advanced U-Net architectures for precise and efficient flood delineation, providing a valuable tool for disaster management and planning. © 2024 IEEE.

关键词： Disaster prevention

来源：评论

学校读者我要写书评

暂无评论

Network Optimization of Large-Scale CNNs for Applications in Autonomous Machines 2

Network Optimization of Large-Scale CNNs for Applications in...

引用

2nd International Conference for Innovation in technology, INOCON 2023

作者： Kambhampaty, Harish Rohan Battula, Naga Datha Saikiran Rentala, Salwesh Challa, Sai Charan Anuradha, K. Gokaraju Rangaraju Institute of Engineering and Technology Computer Science & Engineering Hyderabad India

ISBN: (纸本)9798350320923

In the age of automation, a growing number of devices require high dimensional data to increase the reliability of the machines to better predict and classify objects for autonomous decision-making. 3D object detection directly links to environmental interpretation and therefore builds the base for prediction and movement planning for autonomous systems. With greater computing and storage capacities at the edge level, higher-density data can be captured and analyzed, and hence better predictability of natural environments is achieved. Convolutional Neural Networks are becoming increasingly important for object detection and tracking in many autonomous systems due to the improvement of affordable edge-computing devices capable of working with higher-dimensional data, such as LiDAR sensors, in recent years. Although the capabilities of edge-computing devices are growing, the computational requirements for real-time applications are also rising. This paper proposes two types of optimizations to the networks: pruning and quantization. The proposed optimizations are applied to a base model trained on the KITTI dataset, which shows an average inference speed-up of up to 20% while minimizing the loss of performance. © 2023 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Security Concerns in Cloud and Blockchain Solutions 3

Security Concerns in Cloud and Blockchain Solutions

引用

3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Bundela, Rajmani Dhanda, Namrata Verma, Rajat Zainab, Kaneez Amity University Uttar Pradesh Department of Computer Science and Engineering India Pranveer Singh Institute of Technology Department of Computer Science and Engineering Kanpur India Shri Ramswaroop College of Engineering and Management Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350396157

Cloud computing and blockchain technology are the two transformative technologies that have created significantly impact in various industries. Cloud computing has revolutionized the way resources and services are delivered over the internet, enabling scalable and cost-effective solutions. On the other hand, blockchain has introduced a safe and decentralized structure for recording transactions and maintaining trust in digital interactions. This research study explores the synergy between cloud computing and blockchain, focusing on their combined potential to increase efficiency, privacy, and data security. The integration of cloud computing and blockchain offers several advantages as the technology makes use of the immutability and cryptographic capabilities of blockchain to provide secure and tamper-proof storage of sensitive data. Furthermore, the decentralized structure of blockchain guarantees that data is not under the control of a single entity, enhancing data ownership and privacy. Smart contracts, and programmable scripts on the blockchain, may streamline and automate a variety of cloud-related activities, cutting out mediators and improving productivity. © 2024 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Advances in AI-based genomic data analysis for cancer survival prediction

引用

Multimedia Tools and Applications 2025年第14期84卷 14139-14166页

作者： Deepali Goel, Neelam Padmavati Khandnor University Institute of Engineering and Technology Panjab University Chandigarh160014 India Chandigarh160012 India Department of Computer Science Guru Nanak College Budhlada151502 India

Problem: Cancer is one of the deadliest diseases prevalent in the world. Survivability, early diagnosis, and accurate prognosis are of utmost importance for the therapeutics and clinical management of cancer patients. To achieve accurate and timely prediction of the survival of cancer patients, several machine-learning models based on genomic data have been proposed but a comprehensive review of recent applications in cancer survival prediction is lacking. This paper represents a review of the most recent application of machine learning and deep learning on cancer survival prediction, with a particular focus on the use of genomic data. It specifically targets the most prominent cancer types such as breast cancer, glioblastoma, lung cancer, renal cell cancer, and oral cancer. Methods: A systematic review approach is employed to analyze recent studies on machine learning techniques applied to cancer survival prediction. Emphasis is placed on methodologies utilizing genomic data due to its effectiveness in predicting survival outcomes. Results: This review highlights the efficacy of different machine/deep learning-based techniques in predicting survival outcomes for different cancer types with genomic data. It also provides a summary of the contributions made by different research groups, critically examines the associated challenges, and suggests potential areas for further investigation. Conclusion: Machine learning and deep learning techniques, especially those utilizing genomic data, hold significant promise for accurate cancer survival prediction across diverse cancer types. Despite advancements, challenges such as data heterogeneity and model interpretability still persist. Further research is warranted to address these challenges and develop a comprehensive framework for cancer survival prediction applicable to various cancer types. This review lays the foundation for future investigations in the area of cancer research. © The Author(s), under exclusive licence

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Human-Centered Coordination for Robot-Assisted Equipment Transport

引用

ASME Letters in Dynamic systems and Control 2025年第2期5卷 021002页

作者： Fernandez, Joshua Cosby, Austin Mazumdar, Anirban Department of Mechanical Engineering Georgia Institute of Technology AtlantaGA30363 United States Department of Computer Science Mississippi State University MS39762 United States

This work explores how to use an unmanned ground vehicle (UGV) to offload the physical burdens of equipment from humans. This work formulates dynamic alignment following and compares it to position-based following techniques. We describe the control strategies of both following methods and implement them in a dynamic simulation and a physical prototype. We test the performance of the two following methods and show that dynamic alignment following can reduce robot positional error and interaction force between the human and the robot. We then analyze the energetics and the performance of the human-UGV team for candidate transportation tasks. The presence of the robot can make some tasks take longer to perform. Nonetheless, the results show that for the candidate tasks, the robot can reduce human average metabolic power and average overall task energy. © 2024 by ASME.

关键词： Ground vehicles

来源：评论

学校读者我要写书评

暂无评论

Intelligent Street Light Management System using Solar Panels 3

Intelligent Street Light Management System using Solar Panel...

引用

3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Venkatraman, Akila Ruchitha, Arutla Sellamuthu, Suseela Shalini, Dappu Reddy, Pulsani Dinesh Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Hyderabad India Vellore Institute of Technology Department of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331518592

The conventional street lighting system is used during night time. It consumes more amount of power and there is major energy demand arises. The energy saving methods and lighting control arises to overcome the problem. The smart street light project aims to improve the energy efficiency and urban safety by using sensor- equipped street lights. It can adjust their brightness automatically based on levels of ambient light and the presence of vehicles and pedestrians. Additionally, the project incorporates the central management system implemented by using the ESP 32 module and BLYNK cloud, that allows for remote monitoring and control of street lights. The implementation of this technology can lead to significant cost saving, improves security for the cities and rise the overall quality of life for residents. Vehicle detection system is implemented to control the street lights when vehicle is detected. LED's (Light Emitting Diode) doesn't consume large amount of electricity. By the combination of both LED's and LDR's (Light Dependent Resistor) the intensity of light can be controlled and save more electricity. To make a step towards green energy, solar panels are used to power the street lights. The increased standard of living and growth of cities lead to the complex lighting system with automatic and intelligent control schemes. © 2024 IEEE.

关键词： Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

Groundwater Level Forecasting using Spatio-temporal Graph Convolutional Networks (GCNs) and XGBoost 6

Groundwater Level Forecasting using Spatio-temporal Graph Co...

引用

6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Naga Satish, G. Jadala, Vijaya Chandra Sravani, Kolisetti Giridhara Allam, Sangeetha Shanmuga Sundari, M. Thada, Jyothirmai BVRIT HYDERABAD College of Engineering for Women Hyderabad India Department of Computer Science and Artificial Intelligence School of Computer Science and Artificial Intelligence SR University Warangal506371 India Department of AIML Gokaraju Rangaraju Institute of Engineering And Technology Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798331522667

This research analyzes groundwater levels across multiple districts using data from over 100 observation wells in each district. To capture seasonal variations and predict groundwater behavior, this research has developed three models: periodic, polynomial, and rainfall-based. The periodic and polynomial models describe groundwater level fluctuations based on historical well data, while the rainfall model assesses the influence of precipitation on water levels in the wells. In addition, this research explores advanced predictive techniques by incorporating Spatio-temporal Graph Convolutional Networks (GCNs) and XGBoost. These methods enable a more nuanced understanding of spatio-temporal dependencies and improve predictive accuracy by leveraging both spatial relationships among wells and the temporal evolution of groundwater levels. The integration of traditional models with machine learning techniques aims to enhance groundwater management and inform decision-making. © 2025 IEEE.

关键词： Rain

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：